An Umbrella Review of Artificial Intelligence Applications in Mental Health Care.

Artificial Intelligence in Mental Healthcare: A Scoping Review of Clinical Applications, Evidence, Safety, and Equity

The use of AI scribes across medical fields and why psychiatry is lagging behind: A scoping review.

Objective This scoping review aimed to examine the current available literature on the use of artificial intelligence (AI) scribes across all medical fields compared to their use in psychiatry. Methods A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Five electronic databases (PubMed, MEDLINE, EMBASE, PsycINFO, and CINAHL) were systematically searched for studies published up to June 2025. Two reviewers independently screened titles and abstracts according to pre-established eligibility criteria using the PICOS framework. Full-text articles were then assessed for inclusion. Results A total of 1896 records were identified, of which 15 met the inclusion criteria. Most studies were conducted in outpatient, primary care, and procedural settings. Across studies, clinicians generally reported favorable acceptance and perceptions of AI scribes. Findings also suggested improved clinician well-being, reduced documentation time, better workflow efficiency, and positive patient experience. Results on documentation accuracy and quality were mixed. Critically, no eligible studies evaluated the use of AI scribes in psychiatry. Limitations included study heterogeneity, with wide variations in study design, sample size, and evaluation metrics. Conclusion The absence of empirical studies in psychiatry highlights a significant gap in the literature, particularly in contrast to the growing body of research across other medical specialties. Because of its unique clinical, ethical, and relational dimensions, dedicated research is needed to evaluate the feasibility, safety, and ethical implications of implementing AI scribes in psychiatric care.

Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review

: Artificial intelligence (AI) has increasingly been applied in maternal and child health. However, current evidence remains largely focused on model development, while reports on real-world clinical implementation are limited. This scoping review aimed to map the implementation of AI in maternal and child health settings and summarize reported outcomes. This scoping review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) guideline. Searches were conducted in PubMed and Scopus using Boolean operators combining terms related to artificial intelligence, maternal and child health, and implementation. Studies published in English between 2021 and 2026 were included if they reported clinical or community-based implementation of AI involving real patients or healthcare providers within an ongoing care pathway. Studies focused solely on AI model development or technical validation, reviews, conference abstracts, and editorials were excluded. Of 191 records identified, 156 were screened after duplicate removal, 64 full-text reports were assessed, and seven studies met all inclusion criteria. Identified AI applications were grouped into three themes: maternal support and community-based interventions, neonatal and pediatric monitoring, and screening and diagnostic support. Included studies reported promising outcomes, including improved monitoring accuracy, maternal engagement, and image quality standardization in low-resource settings. However, the seven included studies were highly heterogeneous, and most remained limited to feasibility studies or early-stage implementations. Current evidence suggests AI holds promise as an assistive tool in maternal and child healthcare. However, given the limited number and heterogeneity of included studies, this evidence should be interpreted as preliminary. Organizational, regulatory, financial, and workforce-related barriers, along with ethical considerations such as data privacy and algorithmic bias, remain to be addressed. Further large-scale, long-term implementation studies are needed to evaluate the integration and sustainability of AI in routine maternal and child healthcare practice.

Application of AI in Hypertension Health Education: Scoping Review

This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.

Artificial Intelligence in Mental Health: A Structured Review of Current Evidence across the Care Pathway

It is argued that AI should be framed as an augmentation of - not a replacement for - the clinical relationship, with equity, consent and explainability treated as first-order design constraints.

Review of Artificial Intelligence Applications in Mental Health Diagnosis and Therapy

These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.